TITLE:
Employing Canopy Hyperspectral Narrowband Data and Random Forest Algorithm to Differentiate Palmer Amaranth from Colored Cotton
AUTHORS:
Reginald S. Fletcher, Rickie B. Turley
KEYWORDS:
Amaranthus palmeri, Gossypium hirsutum, Cforest, Machine Learning
JOURNAL NAME:
American Journal of Plant Sciences,
Vol.8 No.12,
November
29,
2017
ABSTRACT: Palmer amaranth (Amaranthus palmeri S. Wats.) invasion
negatively impacts cotton (Gossypium hirsutum L.)
production systems throughout the United States. The objective of this study
was to evaluate canopy hyperspectral narrowband data as input into the random
forest machine learning algorithm to distinguish Palmer amaranth from cotton.
The study focused on differentiating the Palmer amaranth from cotton
near-isogenic lines with bronze, green, and yellow leaves. A spectroradiometer was used
to acquire hyperspectral reflectance measurements of Palmer amaranth and cotton
canopies for two separate dates, December 12, 2016, and May 14, 2017. Data were
collected from plants that were grown in a greenhouse. The spectral data were
aggregated to twenty-four hyperspectral narrowbands proposed for study of
vegetation and agriculture crops. Those bands were tested by the conditional
inference version of random forest (cforest) to differentiate the Palmer
amaranth from cotton. Classifications were binary: Palmer amaranth and cotton
bronze, Palmer amaranth and cotton green, and Palmer amaranth and cotton
yellow. Classification accuracies were verified with overall, user’s, and
producer’s accuracy. For the two dates combined, overall accuracy ranged from
77.8% to 88.9%. The highest overall accuracies were observed for the Palmer
amaranth versus the cotton yellow classification (88.9%, December 12, 2016;
83.3%, May 14, 2017). Producer’s
and user’s accuracies range was 66.7% to 94.4%. Errors were predominately
attributed to cotton being misclassified as Palmer amaranth. The overall
results indicated that cforest has moderate to strong potential for
differentiating Palmer amaranth from cotton when it used hyperspectral
narrowbands known to be useful for vegetation and agricultural surveys as input
variables. This
research further supports using hyperspectral narrowband data and cforest as
decision support tools in cotton production systems.